Data Science is one of the fastest-growing, trending tech career tracks. With such a huge demand for the role, a lot of professionals and graduates are trying to step into this field to quench the demand and build lucrative careers. But with so many options around, it can be over whelming to take the perfect first step into the field of data science. In this article, we will look at all the technical and non-technical prerequisites to kickstart a career in Data Science.
Prerequisites for Becoming a Data Scientist
To become a successful Data Scientist, you need to be proficient in several technical and non-technical skills, some of which are vital to become a Data Scientist, while the others are good to have and will make your life as a Data Scientist easier. Different job roles determine the level of skill-specific proficiency you need to possess.
1. Academic Prerequisites
To become a successful Data Scientist, you need an undergraduate or a postgraduate degree in Computer Science, Mathematics, Statistics, Business Information Systems, Information Management, or any other similar field. This will form a strong foundation for your Data Science career and help you gain the essential skills for processing and analyzing data, and make you capable of stepping into the Data Science industry. By pursuing a degree in any of these fields, you will be exposed to required skills like Coding, Data Structures & Algorithms, Exploratory Data Analysis, Data Visualization, Business Acumen & Business Intelligence, Data Warehousing & Mining, Machine Learning, Model Selection & Evaluation, Predictive Analysis, Scholastic Models, Optimization Techniques, Matrix Computations, and Statistics. The most common degrees that Data Scientists have are Statistics and Mathematics (32%), Business and Economics (21%), Computer Science (19%), and Engineering (16%).
Due to intense competition, many companies prefer candidates with higher education qualifications - either a Master’s degree or a Ph.D. in any of the fields mentioned above. As per KDNuggets, 88% of Data Scientists have at least a Master’s degree and 46% have a Ph.D. On top of an advanced degree, most candidates also undertake online courses to hone their Data Science skills.
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There are also exceptions in the industry, where Data Scientists do not have a Bachelor’s degree or a Master’s degree in a related field, but have an impressive project portfolio, showcasing their skills. One reason for this is the higher demand for Data Scientists in the industry. Anyone can take up online courses to gain the necessary skills, and potentially get a Data Scientist job even without holding a degree in any of the related fields. However, it should be noted that pursuing a career in Data Science without a relevant degree becomes more difficult and competitive. One has to have an exceptional educational background and skills in Data Science that can be gained from any online training.
2. Mathematics / Stastistical Skills
While it is possible to become a Data Scientist without a degree, it is necessary to have Mathematical skills to become a Data Scientist. Data Science is all about dealing with huge datasets, finding trends and patterns, analysis of data, number crunching, and these are derived from the field of Mathematics and Statistics. Let us look at some of the areas in Mathematics that are the prerequisites to becoming a Data Scientist.
2.1 Statistics and Probability
Statistics and Probability form the foundation for Data Science. They are the core of Machine Learning algorithms and are used to analyze data, build models and draw conclusions. If you want to become a successful Data Scientist, you cannot do so without knowledge of these subjects. Statistics is powerful enough to derive valuable insights from data and solve complex business and scientific problems. As a Data Scientist, one needs to carry out different analytical tasks, including predictive analysis, and Statistics and Probability are required for various predictive analytical methods in Machine Learning. Without Statistics, we would have to rely on our emotions and gut reactions for decision making. On the other hand, using statistics can help us in making informed decisions using actionable evidence. We no longer need to rely on our intuition, thereby reducing risk and uncertainty.
Below are some of the topics in Statistics and Probability that are necessary to become a Data Scientist:
- Statistical measures like mean, mode, median, standard deviation, variance, percentiles, and quantiles
- Statistical tests like Hypothesis Testing, p-value, chi-square testing
- Bayes Theorem and Probability Distribution
2.2 Multivariable Calculus
Multivariate Calculus is required to build and optimize many common Machine Learning models. Understanding calculus is the first step to understanding machine learning. It helps in analyzing the relationship between functions and their inputs, and machine learning is all about trying to find inputs that enable a function to best match the data. Machine Learning utilizes concepts from Calculus to understand the relationship between data. As most of the machine learning algorithms are trained on multiple features, we make use of Multivariate Calculus instead of Univariate Calculus. In addition to this, Multivariate Calculus also plays an important role in training a neural network model where the gradient is used to update the model parameters. Specifically, the concept of Partial Derivatives and Gradients from Multivariate Calculus are enough to get started as a Data Scientist.
2.3 Linear Algebra
Every observation in a dataset is modeled as a point in a high-dimensional vector-space. The dataset for most of the machine learning models can be expressed as a matrix which is a concept of Linear Algebra. It is used for Data preprocessing, transformation, and model evaluation. Linear Algebra also forms the foundation of the Data Science career which is why graduates and professionals who are looking to step into the Data Science industry must be familiar with its concepts. These concepts include:
- Vectors, vector spaces, and Matrices
- Transpose, inverse, determinant, and trace of matrices
- Covariance matrix and correlations
- Dot products, eigenvalues and eigenvectors
Linear Algebra provides Data Scientists with a better intuition for choosing hyperparameters while developing a model. Some of the most common Machine Learning concepts like Loss Functions, Principal Component Analysis (PCA), Support Vector Machines (SVM), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Image Convolution are derived from concepts of Linear Algebra.
2.4 Optimization Methods
As the name suggests, optimization methods mean methods that help us in maximizing or minimizing the value of a function by choosing input values from an allowed domain and computing the output value of the function. But why are they important in Data Science? Optimization Methods help us find the best possible solution for a problem. Specifically, in the case of a machine learning model, it helps us in finding the best hyperparameters for the model. This in turn helps us improve the efficiency of our model. The most common use of Optimization Methods in Machine Learning is the Loss function where we constantly try to reduce the value of the loss observed.
Almost all the algorithms in Machine Learning can be thought of as a solution to an optimization problem. The reason behind this is because Machine learning involves using an algorithm to learn from the dataset and make predictions on new data. For this, we need to find an approximate function that maps the dataset input values to the respective output values. Here, we make use of a parameterized mapping function, i.e each input variable is assigned a weight, formally known as a hyperparameter, and using an optimization algorithm, we find the parameters that result in minimum deviation of the calculated output values from the expected values. Thus, every time we fit a machine learning algorithm on a training dataset, we solve an optimization problem.
3. Programming Prerequisites for Data Science
To become a Data Scientist, programming is another skill that is necessary. Data Scientists typically use languages like Python, R, and SQL. As compared to a Software Developer, Data Scientists do not need in-depth knowledge of programming. Being familiar with the basics of the language is enough to get a job in Data Science as long as you are comfortable in writing efficient code in any language.
3.1 Skills in Python
Python is one of the highly required and one of the most popular programming languages among Data Scientists. Being a versatile language, it can be used in all stages of Data Science - including data mining or running applications. It is a multi-purpose and object-oriented programming language that is very easy to learn. Python has a vast open-source library with powerful Data Science libraries available like Numpy, Pandas, Matplotlib, PyTorch, Keras, Scikit Learn, Seaborn, etc. These libraries help with various Data Science tasks like reading huge datasets, plotting and visualizing data and correlations, training and fitting machine learning models for your data, evaluating the performance of the model, etc.
3.2 Skills in R
R is an open-source programming language specifically designed for Data Science and widely used for statistical analysis. After Python, it is the language that is highly in demand for Data Science jobs. R has tools for presenting and communicating data-driven results and it might be more suited for research and academic work. Like Python, R can also be used to solve any Data Science related problem. However, unlike Python, it is not easy to learn, especially if you already have expertise in any other programming language. It has a very steep learning curve. R offers support for data visualization, statistical methods, machine learning, etc.
Usually, it is not needed to have expertise in both Python and R. Having a sound knowledge of either of these programming languages is enough to have a successful career in Data Science.
Excel is another very important prerequisite for Data Science. It is an important tool to understand, manipulate, analyze and visualize data. An Excel Spreadsheet allows us to organize raw data into a readable format, making it one of the most intelligent ways to extract actionable insights. Generally, people are already familiar with Excel, thus it requires very little effort to become an expert in it. Excel is great to use when a lot of manipulations and computations have to be done on the data. It also provides the ability to customize fields and out-of-the-box functions to perform calculations. Even if you have large data sets, Excel makes it possible to visualize segmented data without having to use any other software.
SQL is another important prerequisite skill required for Data Science. In comparison to other programming languages, SQL is not very complex but a must-have skill to be proficient in, to become a Data Scientist. This programming language is used to manage and query data that is stored in relational databases. Using SQL, we can fetch, insert, update or delete data. It also allows you to query multiple tables at once using the join operation. To extract insights from data, it is important to know how to write complex SQL queries involving joins, group by, having, etc. SQL also provides the ability to carry out analytical functions and transform database structures.
4. Technical Skills
Moving forward, let us move to the next set of requirements which are the technical skills that are prerequisites to learn Data Science.
4.1 Data Science
While Data Scientists need familiarity in mathematics, statistics, and programming, it is extremely important to know Data Science concepts and tools. Hadoop, Apache Spark, Data Visualization tools are a few of the Data Science skills necessary to become a Data Scientist.
As Data Scientists deal with huge volumes of data, sometimes the memory of the system might not be enough to carry out the processing. In such a scenario, Hadoop comes to the rescue. It can be used to quickly partition and send data to different servers for data processing and performing various operations like filtering. As Hadoop is based on the concept of Distributed Computing, some companies prefer Data Scientists to know basic Distributed System concepts such as Pig, Hive, MapReduce, etc. Some companies have started to switch to Hadoop-as-a-Service (HaaS), another terminology for Hadoop on the cloud so Data Scientists need not know the in-depth working of Hadoop.
4.3 Apache Spark
Apache Spark is a Big Data computation framework like Hadoop, and is very popular in the Data Science world. While Hadoop reads data from and writes data to disk, Spark caches the computation results in the system memory, making it comparatively faster than Hadoop. Apache Spark is designed specifically for Data Science and it facilitates running complicated algorithms faster. It helps in handling complex, large and unstructured datasets while making it possible to prevent data loss. It also helps in saving time by distributing data processing when the dataset size is large. The main benefits of using Apache Spark are its speed and the platform provided to easily run Data Science tasks and processes. It is possible to run Spark on a single machine or a cluster of machines.
4.4 Data Visualization
As the business world generates a large amount of data daily, there is a need to translate this data into a format that can be easily understood. Data Visualization does exactly this and is very effective in understanding the data as humans can comprehend pictures more easily than raw data. Thus, Data Visualization becomes very important in the Data Science market. Using Data Visualization, we can represent data visually through graphs, charts, and maps. There are various tools for this purpose like Tableau, Chartist, etc. Some Data Scientists also prefer using Python and R for visualization over the standard Visualization tools, as these languages also offer libraries like ggplot, matplotlib that can help in plotting datasets. By visualizing the data, it is possible to perform complex data analysis, understand the data, identify trends, and quickly grasp insights to act on business opportunities.
4.5 Machine Learning
As Machine Learning algorithms are an excellent way to analyze large amounts of data, this makes it an integral part of any Data Science career. It can help in automating a lot of tasks involved in a Data Science job. However, in-depth knowledge of Machine Learning concepts in advance is not mandatory to start a career in this field. Most Data Scientists do not have expertise in Machine Learning concepts. Only a small percentage of Data Scientists are highly familiar and skilled in advanced concepts like Recommendation Engines, Adversarial Learning, Reinforcement Learning, Natural Language Processing, Outlier Detection, Time Series Analysis, Computer Vision, Survival Analysis, etc. Skills in these concepts, therefore, will help you stand out in your Data Science career.
4.6 Working with Unstructured Data
Data Scientists deal with data daily which could be either structured or unstructured. Unstructured data, unlike structured data, cannot be stored in relational database tables and is not streamlined. Videos, audios, images, text, and articles are all forms of unstructured data and this form of data can come from any channel and source. Social media is one of the most common sources of unstructured data. With the rise of Big Data and the internet, the amount of unstructured data available has grown beyond imagination. Thus, the ability to work with unstructured data is a skill that is vital for a Data Scientist. Although working with unstructured data is highly complex, it can help unravel insights that are useful in decision-making.
5. Non-Technical Skills
Having discussed the Educational, Mathematical, Programming, and Technical prerequisites to become a Data Scientist, let us now move to the last set of prerequisites - the non-technical requirements. As Data Scientists are the link between business goals and product strategy, having these non-technical skills become important.
5.1 Business Acumen
As Data Science aims to solve business problems, Data Scientists must have an understanding of the industry, the problems faced by the business that need to be solved, and also the impact of solving this problem. Thus, Data Scientists should be familiar with how businesses operate so that they can use the data to efficiently help the business. Data Scientists need strong business acumen to be able to discern the problem and challenges that need to be tackled for the business to grow and run smoothly.
5.2 Management Principles
Data Science is a job that requires interpersonal and management skills like high collaboration, the ability to work in teams, and presentation skills. Data Scientists need to collaborate with different team members, including product managers, designers, developers, executives as well as clients to generate better business solutions and strategies. These solutions and strategies have the potential to impact the growth and performance of the business and they need to be presented to stakeholders, clients, and other departments. Thus, it is crucial to have good presentation and management skills.
While Data Scientists have the technical skills required to extract and analyze data, they should also be able to communicate their technical findings fluently, clearly, and effectively to other teams like Sales, Operations, or Marketing, where members might not have the same professional background. Good communication skills are important to make better business decisions. One of the Data Science job roles, Data Storyteller, requires the ability to create a storyline around the data to make it easy for anyone to understand. Storytelling is an effective way to properly communicate the findings to others.
5.4 Data Intuition
Data Intuition, unlike other data science prerequisites, can be gained from experience and the right training. Data Scientists require to have this intuition to know where to look for insightful information. This is because, in the case of large datasets, valuable insights are not always apparent. Having a strong data intuition helps Data Scientists to be efficient in their tasks, which is why this is one of the important non-technical prerequisites.
Get, Set, Grow!
These are some of the steps one can take to lay the foundation of a career in Data Science. All of the above data science prerequsites are suggestions based on what paths professionals in the industry have followed, and these are good places to start and build one’s data science skills. We hope this article provides you the answers you were looking for. You can always use the comments section to share your views or fill our contact form to speak to our career advisors.